41 citations · 83 across the 8 of their papers we have counts for
17 papers
Noise-resilient approach for deep tomographic imaging
Zhen Guo, Zhiguang Liu, Qihang Zhang +2
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantage…
Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
Fabian A. Braeu, Alexandre H. Thiéry, Tin A. Tun +4
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is th…
Machine Learning Regularized Solution of the Lippmann-Schwinger Equation
Subeen Pang, George Barbastathis
Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regula…
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
Raj Dandekar, Chris Rackauckas, George Barbastathis
We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon p…
On the interplay between physical and content priors in deep learning for computational imaging
Mo Deng, Shuai Li, Iksung Kang +2
Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two im…
Neural Network aided quarantine control model estimation of global Covid-19 spread
Raj Dandekar, George Barbastathis
Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019, the disease has spread worldwide and met with a wide variety of so…